The effects of events on time-stamped data may be an important factor in creating an accurate computer model of the data, for example, to improve the predictive ability of the model. Examples of events that may affect time-stamped data include advertising campaigns, retail promotions, strikes, natural disasters, policy changes, a data recording error, etc.
The characteristics of an event may define the effect that an occurrence has on the time-stamped data and the duration of the occurrence. An event may include a single occurrence (e.g., a worker strike), multiple occurrences (e.g., a holiday), or irregular occurrences (e.g., new store openings.) Further, an occurrence may be on a fixed date or a relative date (e.g., an event may begin two weeks before Christmas.) In addition, an event may be a combination of occurrences (e.g., a retail sale that occurs over a holiday.) Thus, modeling events so that they increase the accuracy of a time series model can often be complicated.